2 citations · 9 across the 46 of their papers we have counts for
47 papers
Conformalized Quantile Regression and Minimax Limits of Fixed-Score Calibration under Known Covariate Shift
Rustam Isaev, Anton Conrad, Denis Belomestny +2
In this paper, we study nonasymptotic error bounds for interval length and conditional coverage in split conformalized quantile regression (CQR). Our bounds rely on local reg…
Optimizers for Diffusion Models: A Controlled Benchmark
Arman Bolatov, Egor Shulgin, David Li +6
Discrete diffusion models now match autoregressive language models on several benchmarks, while the question of how best to train them has received far less attention: the optimize…
Beyond Marginal Validity: Finite-Sample Guarantees for Localized Conformal Prediction
Anton Conrad, Rustam Isaev, Denis Belomestny +2
Conformal prediction endows arbitrary black-box predictors with finite-sample, distribution-free marginal coverage, yet marginal validity can hide severe covariate-specific miscali…
Beyond Softmax and Entropy: Convergence Rates of Policy Gradients with f-SoftArgmax Parameterization & Coupled Regularization
Safwan Labbi, Daniil Tiapkin, Paul Mangold +1
Policy gradient methods are known to be highly sensitive to the choice of policy parameterization. In particular, the widely used softmax parameterization can induce ill-conditione…
Convergence Guarantees for Federated SARSA with Local Training and Heterogeneous Agents
Paul Mangold, Eloïse Berthier, Eric Moulines
We present a novel theoretical analysis of Federated SARSA (FedSARSA) with linear function approximation and local training. We establish convergence guarantees for FedSARSA in the…
Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation
Bogdan Butyrin, Eric Moulines, Alexey Naumov +3
In this paper, we refine the Berry-Esseen bounds for the multivariate normal approximation of Polyak-Ruppert averaged iterates arising from the linear stochastic approximation (LSA…
Neural Optimal Transport Meets Multivariate Conformal Prediction
Vladimir Kondratyev, Alexander Fishkov, Nikita Kotelevskii +4
We propose a framework for conditional vector quantile regression (CVQR) that combines neural optimal transport with amortized optimization, and apply it to multivariate conformal…
Multidimensional Uncertainty Quantification via Optimal Transport
Nikita Kotelevskii, Maiya Goloburda, Vladimir Kondratyev +4
Most uncertainty quantification (UQ) approaches provide a single scalar value as a measure of model reliability. However, different uncertainty measures could provide complementary…
Improving French Synthetic Speech Quality via SSML Prosody Control
Nassima Ould Ouali, Awais Hussain Sani, Ruben Bueno +3
Despite recent advances, synthetic voices often lack expressiveness due to limited prosody control in commercial text-to-speech (TTS) systems. We introduce the first end-to-end pip…
Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games
Antonio Ocello, Daniil Tiapkin, Lorenzo Mancini +2
We introduce Mean-Field Trust Region Policy Optimization (MF-TRPO), a novel algorithm designed to compute approximate Nash equilibria for ergodic Mean-Field Games (MFG) in finite s…
Statistical inference for Linear Stochastic Approximation with Markovian Noise
Sergey Samsonov, Marina Sheshukova, Eric Moulines +1
In this paper we derive non-asymptotic Berry-Esseen bounds for Polyak-Ruppert averaged iterates of the Linear Stochastic Approximation (LSA) algorithm driven by the Markovian noise…
A note on concentration inequalities for the overlapped batch mean variance estimators for Markov chains
Eric Moulines, Alexey Naumov, Sergey Samsonov
In this paper, we study the concentration properties of quadratic forms associated with Markov chains using the martingale decomposition method introduced by Atchadé and Cattaneo (…
On Global Convergence Rates for Federated Softmax Policy Gradient under Heterogeneous Environments
Safwan Labbi, Paul Mangold, Daniil Tiapkin +1
We provide global convergence rates for vanilla and entropy-regularized federated softmax stochastic policy gradient (FedPG) with local training. We show that FedPG converges to a…
Proximal Point Nash Learning from Human Feedback
Daniil Tiapkin, Daniele Calandriello, Denis Belomestny +5
Traditional Reinforcement Learning from Human Feedback (RLHF) often relies on reward models, frequently assuming preference structures like the Bradley--Terry model, which may not…
Adaptive Cumulative Mass Calibration with Conformal Prediction
Daniil Kazantsev, Eric Moulines, Maxim Panov +2
Reliable probability estimates by classifiers are essential in high-risk applications. In practice, however, predicted probabilities are often miscalibrated, and many existing post…
Online Decision-Focused Learning
Aymeric Capitaine, Maxime Haddouche, Eric Moulines +3
Decision-focused learning (DFL) is an increasingly popular paradigm for training predictive models whose outputs are used in decision-making tasks. Instead of merely optimizing for…
Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up
Paul Mangold, Alain Durmus, Aymeric Dieuleveut +1
This paper proposes a novel analysis for the Scaffold algorithm, a popular method for dealing with data heterogeneity in federated learning. While its convergence in deterministic…
Rectifying Conformity Scores for Better Conditional Coverage
Vincent Plassier, Alexander Fishkov, Victor Dheur +4
We present a new method for generating confidence sets within the split conformal prediction framework. Our method performs a trainable transformation of any given conformity score…
Optimizing Asynchronous Federated Learning: A Delicate Trade-Off Between Model-Parameter Staleness and Update Frequency
Abdelkrim Alahyane, Céline Comte, Matthieu Jonckheere +1
Synchronous federated learning (FL) scales poorly with the number of clients due to the straggler effect. Algorithms like FedAsync and GeneralizedFedAsync address this limitation b…
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent
Marina Sheshukova, Sergey Samsonov, Denis Belomestny +4
In this paper, we establish the non-asymptotic validity of the multiplier bootstrap procedure for constructing the confidence sets using the Stochastic Gradient Descent (SGD) algor…
Online Decision-Making in Tree-Like Multi-Agent Games with Transfers
Antoine Scheid, Etienne Boursier, Alain Durmus +2
The widespread deployment of Machine Learning systems everywhere raises challenges, such as dealing with interactions or competition between multiple learners. In that goal, we stu…
Prediction-Aware Learning in Multi-Agent Systems
Aymeric Capitaine, Etienne Boursier, Eric Moulines +2
The framework of uncoupled online learning in multiplayer games has made significant progress in recent years. In particular, the development of time-varying games has considerably…
Refined Analysis of Federated Averaging and Federated Richardson-Romberg
Paul Mangold, Alain Durmus, Aymeric Dieuleveut +2
In this paper, we present a novel analysis of \FedAvg with constant step size, relying on the Markov property of the underlying process. We demonstrate that the global iterates of…
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents
Safwan Labbi, Daniil Tiapkin, Lorenzo Mancini +2
In this paper, we present the Federated Upper Confidence Bound Value Iteration algorithm (), a novel extension of the algorithm (Azar et al., 2…